Methods and data structures for improved searchable formatted documents including citation and corpus generation

ABSTRACT

Searchable annotated formatted documents are produced by correlating documents stored as a photographic or scanned graphic representations of an actual document (evidence, report, court order, etc.) with textual version of the same documents. A produced document will provide additional details in a data structure that supports citation annotation as well as other types of analysis of a document. The data structure also supports generation of citation reports and corpus reports. A method of creating searchable annotated formatted documents including citation and corpus reports by correlating and correcting text files with photographic or scanned graphic of the original documents. Data structures for correlating and correcting text files with graphic images. Generation of citation reports, concordance reports, and corpus reports. Data structures for citation reports, concordance reports, and corpus reports generation.

RELATED APPLICATION

This application claims priority under 35 U.S.C. §199(e) of the co-pending U.S. provisional application Ser. No. 61/192,169, filed Sep. 16, 2008, entitled “METHODS AND DATA STRUCTURES FOR IMPROVED SEARCHABLE FORMATTED DOCUMENTS INCLUDING CITATION AND CORPUS GENERATION,” which is hereby incorporated by reference.

FIELD OF THE INVENTION

This invention relates to improved searchable formatted electronic documents and analysis tools, such as citation and corpus generation. Examples of documents include patents, patent applications, evidence files, and other documents which are available in graphic form and optionally also available in a text form.

BACKGROUND OF THE INVENTION

In the field of electronic document management there are many situations where a document is stored as a photographic or scanned graphic of the actual document. For example, in a litigation document management system example documents may represent evidence, reports, court orders, patent documents, etc. The graphic image of the page is critical in many cases and needs to be preserved. However, there is also a need to electronically search the document. Additionally, there has been a long felt need to be able to cut the text from a document and have an accurate internal citation, or location identification, automatically pasted into a new document (e.g. report, brief, etc.) with the text that was cut. In litigation, having analysis, reports, and arguments error free is very important and a significant amount of time spent creating quotes and internal citations and then in checking them to ensure accuracy.

What is needed is a way to analyze documents in its graphic format and then be able to generate quotations with accurate internal citations. Also in patent analysis, for example, what also is needed is a way to thoroughly review all occurrences of certain terms in context to be able to thoroughly and accurately determine the meaning of those terms.

SUMMARY OF THE INVENTION

The current invention provides the ability to produce searchable annotated formatted documents by correlating documents stored as a photographic or scanned graphic representations of an actual document (evidence, report, court order, etc.) with textual version of the same documents. A produced document will provide additional details using data structure(s) that would support the above described citation annotation as well as other types of analysis of a document. The data structure(s) also support generation of citation and corpus reports.

Objects and Advantages

Accordingly, beside the objects and advantages described above, some additional objects and advantages of the present invention are:

-   1. To provide a quicker and more effective method analyzing     documents. -   2. To provide a highly accurate, electronically searchable document     from graphic images of the document pages. -   3. To provide means and methods of document analysis that are easy     to use. -   4. To reduce the cost of document analysis. -   5. To reduce the cost of evidence analysis. -   6. To improve the thoroughness of documents analysis. -   7. To improve the thoroughness of patent claim term analysis. -   8. To identify inconsistencies in the use of document terms, e.g.     usage of disputed patent claim terms.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a process by which remote documents are obtained and collected and then subsequently converted to other formats.

FIG. 1B illustrates a process by which a searchable annotated formatted document is produced.

FIG. 2A illustrates data structures that are used to verify, correlate and correct meta-data data, for example, patent OCR data.

FIG. 2B illustrates an embodiment of a text data structure.

FIG. 2C illustrates an embodiment of a meta-data data structure.

FIG. 3A illustrates a text parse routine.

FIG. 3B illustrates a searchable formatted document parse routine.

FIG. 4 illustrates a correlate and correct routine.

FIG. 5 illustrates an annotation routine.

FIG. 6 illustrates generation of a concordance.

FIG. 7A illustrates generation of a word corpus.

FIG. 7B illustrates an embodiment of word corpus.

FIG. 8A illustrates the generation of a citation report.

FIG. 8B illustrates an embodiment of a citation report.

REFERENCE NUMERALS IN DRAWINGS

-   1 local computer -   2 network -   3 remote computer -   4 network accessible file collection -   5 converted file -   6 output device -   10 graphic images -   20 text file -   30 formatted document -   40 searchable formatted document -   50 corrected formatted document -   60 searchable annotated formatted document -   62 parse routine -   64 correlate and correct routine -   66 attach data routine -   68 tag words and sentences routine -   71 relative fields -   72 citation fields -   73 part fields -   74 section fields -   75 text for sentences fields -   76 doc ID field -   80 text field -   81 sequential paragraph number -   82 sequential sentence number -   83 new paragraph start -   84 citation start column # field -   85 citation start line # field -   86 citation start word # field -   87 citation end column # field -   88 citation end line # field -   89 citation end word # field -   90 part field -   91 section field -   92 specification part -   98 doc ID field -   102 OCR page # -   104 OCR column # -   106 OCR line # -   108 OCR line coordinates -   110 OCR new paragraph start -   112 OCR line font size -   114 OCR line text -   116 OCR line top coordinate -   118 OCR line bottom coordinate -   120 OCR line left coordinate -   122 OCR line right coordinate -   200 data structure(s) -   200 a first (text) data structure -   200 b second (meta-data) data structure -   302 read text -   304 determine document parts -   306 determine document sections -   308 determine new paragraphs -   310 determine paragraph types -   312 apply relative numbers to each section -   314 read text output -   316 determine parts output -   318 determine sections output -   320 determine new paragraph output -   322 determine paragraph type output -   324 re-apply relative numbers to each section -   326 read searchable formatted document -   328 assemble lines -   330 allocate lines to columns -   332 calculate line numbers -   334 read searchable formatted document output -   336 assemble lines output -   338 allocate lines to columns output -   402 read text file -   404 read searchable formatted document -   406 match text -   408 determine column line and word values -   410 contains figures? -   412 determine figure # and item # -   414 contains claims? -   416 determine claim and clause # -   420 read text file output -   422 read searchable formatted document output -   424 match text output -   426 determine column line and word values output -   428 determine figure # and item #output -   600 concordance program -   602 concordance -   700 corpus program -   702 word or phrase corpus -   710 prior content column -   712 word (or phrase) column -   714 subsequent content column -   716 citation column -   720 heading row -   722 corpus sample row 1 -   724 corpus sample row 2 -   728 corpus last sample row -   800 citation program -   802 citation document -   804 citation document title -   810(a-d) citation document section title -   812(a-d) citation sentence -   814(a-d) citation annotation

SPECIAL DEFINITIONS

corpus—a collection of recorded statements used as a basis for the descriptive analysis of language in a written document

concordance—an index of the important words used in a written document

annotation—extra information which is not normally displayed, such as citation information from a data structure that provides citations for text cut from a formatted document

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1A illustrates a process by which remote documents are obtained and collected and then subsequently converted to other formats. Local computer 1 connects to a remote computer 3 via a network 2. Then it accesses file data from the network accessible file collection 4 and retrieves the desired files onto the local computer 1 over the network 2. Once on the local computer 1, the files can be converted into a single converted file 5. Once converted, the file can be output to a peripheral device 6, such as a display or a printer (as shown).

For example, the United States Patent and Trademark Office (USPTO) has a service which provides patent publications as TIFF files, one file for each page.

A patent related embodiment performs the following steps:

-   -   a) Input a patent number     -   b) Access the USPTO World Wide Web site over the Internet to         obtain the HTML version of a patent and to use that HTML to         determine the number of pages represented by graphic images         (e.g. TIFF)     -   c) Download each page's graphic image     -   d) Convert the collection of graphic images into a single         document (e.g. PDF or multipart TIFF).     -   e) Optically recognize (via OCR) the page graphic images.         Alternatively, each page's graphic image can be processed on the         fly (e.g. recognized as each is downloaded in step c above).

These steps could be implemented in computer software. Example embodiments include an Acrobat plug-in or a World Wide Web browser plug-in. Good results have also been obtained implementing these steps as a script running on a group of computers including one computer running an OCR engine (such as OmniPage, TextBridge, or other commercially available OCR engine) and another computer running an Oracle database.

A novel improvement in the system illustrated in FIG. 1A is that an HTML (or plain text) version of the same subject matter can be used to correct spelling in an OCR document created from graphic page images (e.g. using Acrobat OCR capture). For example, in the embodiment for U.S. patents, the USPTO also provides an HTML version of the patent which can be used to correct and correlated the OCR text. It is well known that OCR is not 100% perfect and human comparison and correction is costly. This aspect of the system can significantly improve the value of searchable documents created by OCR.

Once the graphic images are converted (and optionally corrected), the document 5 can be printed, for example on printer 6.

FIG. 1B illustrates production of a searchable annotated formatted document 60. The U.S. patent example will be used to illustrate this aspect of the system. The USPTO provides a graphic image 10 for each page of a published patent. These graphic images 10 (e.g. TUT files) are not text searchable (they are like photographs). A formatted document 30, such as an Adobe Acrobat PDF, can be created as a binder holding every graphic image 10 (routine A). See also FIG. 1A. The formatted document 30 (e.g. PDF file) can be processed with OCR (routine B) to convert the images to searchable text forming a searchable formatted document 40. Alternatively, an OCR engine can provide structured data which describes the text elements found in the document with the graphic location of each element, and which can be used instead of searchable formatted document 40. The USPTO also provides a text file 20 in HTML format for many patents. The text file 20 is electronically searchable. The searchable formatted document 40 is correlated with the text file 20 to correct the OCR text (routine C) resulting in a corrected formatted document 50.

The corrected formatted document 50 is a valuable tool for analysis.

Further, the corrected formatted document 50 can be used to add various annotations (routine D) to produce a searchable annotated formatted document 60. “Annotated” is not used in the general sense as would be understood by one of skill in the art. The word is used here in a new limited way to refer to the annotations from the data structure(s) 200 that provide the internal citations for quotes pasted from the searchable annotated formatted document 60. Again using the U.S. patent example, the searchable annotated formatted document 60 version of a patent can be used as the primary analysis document in a patent litigation or evaluation. When an expert or attorney wants to refer to a particular section of the patent, the user simply selects the desired words and a highly accurate quote and citation, including for example, column and line numbers, is automatically generated and can be pasted into a report or brief.

FIG. 2A illustrates data structures that are used to verify, correlate and correct document data, for example, patent OCR data. The data structures 200 can be implemented in various ways. Good results have been obtained by implementing them as Microsoft Excel spreadsheets, perl data structures, XML files, or Oracle database data tables.

The first data structure 200 a generally contains each unit with information used to provide an internal citation, e.g. “('498 Patent 12:47-13:5)”. An example of a unit would be a sentence or a title. This data is substantially obtained from the HTML (or text version of the document) and may contain estimated values. In one embodiment, a spreadsheet document (such as Microsoft Excel) with functions is used to estimate the citations (to improve correlation ease or accuracy).

The second data structure 200b generally contains each line (or column line). Each column line has only one column and/or line number associated with it. This data preferably comes from OCR data (but may be input by people).

The data between these two structures are correlated with a unique ID for each entry in the tables. The sentence data has an ID for each sentence. The column line data has an ID for each line. The line data is matched, if possible, to each sentence's unique ID and give a sentence a relative ordinal number. The actual citation data, such as start column, start line, end column, and end line, is filled in the first data structure 200 a based on matching first and last line data. The OCR text is corrected by verification against the HMTL data. Missing data in the data structure is flagged and estimates are used.

FIG. 2B illustrates an exemplary text data structure 200 a that holds information that will be used to provide the citations for quotes pasted from the searchable annotated formatted document 60 or citations on a word or phrase corpus 702. The text data structure 200 a also provides an alternative output that can be used independently from the formatted documents (30,40,50 or 60). The text data structure 200 a is first instantiated by a parsing routine (FIG. 3) and updated by a correlation and correction routine (FIG. 4).

FIG. 2C illustrates an exemplary meta-data data structure 200 b that holds information that will be used to as an interim repository for the data parsed from the searchable formatted document 40, or alternatively obtained from OCR.

FIG. 3A illustrates a text parse routine (which could be implemented in Perl, for example) which reads the text from the text file 20 or another source of text (step 302). It determines the document parts (step 304). For example, in a U.S. patent embodiment, parts could be one of Abstract, Drawing, Specification, or Claims. Then it determines document sections (step 306). For example, in a U.S. patent embodiment, document sections include Background of the Invention, Summary of the Invention section, and so forth. Then it determines new paragraphs (step 308). Then it determines paragraphs types (step 310). For example, regular paragraph text, tables, equations, etc. In step 312, the text data structure 200 a in FIG. 2B would be filled in except for the citation fields (72). The text for each heading or sentence would be filled in the text field (80). Relative numbers would be filled in, for example in the specification part (92), each paragraph would be assigned a sequential paragraph number (81) and sequential sentence number (82). Sentence numbers would restart at 1 for each paragraph. In addition, any new paragraph starts (83) would be identified. In the patent drawings, for example, any text from the figures would be indexed. In the claims, each clause would be relative to the claim. The part fields (73,90) and the section fields (74, 91) would be filled in.

At this point the text data structure 200 a (without the citations) has an alternative use. It can be used to produce a text version of the subject document (e.g. patent) with numbered paragraphs (e.g. for electronic filing) e.g. “[103] Referring to FIG. 1B . . . ”

FIG. 3B illustrates the searchable formatted document parse routine (which could be implemented in Perl, for example) which reads the searchable formatted document 40 (step 326). Then it takes the words and assembles the lines (step 328). Then it allocates each line to either the left or right column (step 330). Then it calculates the line numbers for each line (step 332). At this point, the meta-data data structure 200 b in FIG. 2C would be filled in completely.

FIG. 5 illustrates the annotation routine which is an implementation of routine D in FIG. 1B. First the text (20 or 50) is parsed by the parse routine 62 (e.g. using the text parse routine shown in FIG. 3A) and the searchable formatted document parse routine (e.g. see FIG. 3B). Next the text data structure 200 a is updated with correlation and correction information by the correlate and correct routine 64. For example, see FIG. 4.

The correlation information in the patent example would be the citation column, line and word numbers for the start and end of each sentence. The end could be optional because, for example, it could be determined by looking at the next record's start. The next step is to attach the data (e.g. FIG. 2B) by the attach data routine 66 to the document. In some embodiments, the data would be meta-data that is not normally viewable in normal display modes. In other embodiments, the data in each record could be stored as a user displayable “annotation” (in the general sense) at the user's option. In other embodiments, the data is simply appended to the end of the document e.g. as a table.

Next the words and/or sentences are tagged by the tag words and sentences routine (68) in the formatted document so that when a portion is cut or copied, the citation associated with the text is cut or copied with it. Special software associated with the document viewer would handle the “cut” and/or “copy” operation. For example, in PDF or FrameMaker or Microsoft Word a plug-in could be added (either by the publisher or as a 3^(rd) party plug-in) to provide text plus the citation (e.g. “U.S. Pat. No. 8,888,523 17:40-18:3”) in the paste buffer.

Note that step 68 in FIG. 5 could be optional because the plug-in could use the attached data (step 66) to provide the citation in the paste buffer. However, by tagging the documents, the determination of the citation for a particular set of words is more straightforward at the time of the cut and/or copy operation.

FIG. 4 illustrates an example of the correlate and correct step 64 of FIG. 5. A text file 20 is read (step 402). A searchable formatted document 40 is read (step 404). The order of steps 402 and 404 is not significant, and may be performed in parallel. In step 406, the text file 20 in the form of the text data structure 200 a is compared with a searchable formatted document 40 in the form of the meta-data data structure 200 b. The relative text is matched to the positional information in the graphic version of the same document to determine page or column number, line number, and word number (92) or the line (step 408). If the document contains figures (decision 410), then the figure numbers and item numbers (94) are determined in step 412. If the document contains claims (decision 414), then the claim numbers and clause numbers are also determined in step 416. Thus the correlation routine updates the text data structure 200 a (FIG. 2B) with the start (and optionally end) citation information 72 (84-89).

If the document or page has a document ID (e.g. a BATES number), it is added in (76, 98). After the correlate and correct routine (e.g. for example, see FIG. 4) is complete the data structure can be used in many ways including but not limited to:

-   1. Annotation of the corrected formatted document 50 to form the     searchable annotated formatted document 60. -   2. Production of a spreadsheet containing each text element and     citation where by a reader can find text in the spreadsheet by     electronically searching and quickly find the internal citation or     location of the text in a paper version. -   3. Input to a concordance program 600 that identifies each     occurrence of a word or phrase and produces a concordance 602 with     each sentence with the specified word or phrase and its citation     (FIG. 6). -   4. Input to a corpus program 700 that identifies each occurrence of     a word or phrase and produces a corpus 702 with each sentence and     with the specified word or phrase and its citation (FIG. 7A). FIG.     7B shows an embodiment of word or phrase corpus 702. -   5. Input to a citation program 800 that produces a citation document     802 with each sentence and with its citation (FIG. 8A). FIG. 8B     shows an embodiment of a citation document 802.

Note the word “corpus” generally means “entire body.” In the context of the field of language analysis it refers to a collection of recorded utterances used as a basis for the descriptive analysis of a language. In the context of this invention the word “corpus” (as well as “concordance”) is limited to the language used in a single document (e.g. a patent) or a small group of related documents (e.g. a set of related patents with common inventorship or subject matter).

The corpus 702 could be corpus for a single word or phrase i.e. a “word corpus” or for all the words in the document i.e. a “document corpus” or for a set of key words (e.g. a) similar to those selected for a concordance, or b) a set of disputed terms). This invention is not limited to patent analysis. It is also useful for analysis of other evidence, such as e-mail, source code, contracts, discovery documents, Web pages, contracts, etc.

The methods for:

-   -   generating a corrected formatted document 50 (e.g. FIG. 1B),     -   generating a searchable annotated formatted document 60 (e.g.         FIG. 1B),     -   generating a concordance 602 (e.g. FIG. 6),     -   generating a corpus 702 (e.g. FIG. 7A), and     -   generating a citation document 802 (e.g. FIG. 8A),         can each be implement on a web site enabled by a database for         storing the correlation data and various generated documents.

Advantages Searchable

Text searches can be performed within the document or across multiple documents.

Copy and Paste Citation

Each sentence in the document can be copied and pasted into other documents. The internal citation for each sentence (or group of sentences) is copied and pasted along with the sentence.

Original Document Format Maintained

The original format of the document is maintained after it has been converted to a searchable format.

Identify Location in Original Document

As formats in the text file 20 may differ from the original formatted document 30, it is important that the annotations or citations correlate with each sentence and/or line and/or heading to identify its relative location with a document.

Accuracy

Optical Character Recognition (OCR) systems have struggled for decades to improve accuracy. OCR does very poorly on some documents. Further, for some situations, such as an expert witness report or Federal Court brief, errors can have severe impact on the perception of the facts in the case and ultimately cost millions of dollars. The present invention provides a way to improve accuracy of the computer generated data as well as improve the accuracy of human construction of reports and briefs.

Thorough

The concordance and corpus features of present invention provide a means for through analysis of every instance of a word or phase, or related phrases, in a document of related set of documents. For example, in a patent case, the corpus documents on disputed claim terms will not only make claim term analysis more efficient, but will also ensure that all usage is considered and inconsistencies are understood. The use of corpus reports will improve the rigorousness of claim term analysis and provide judges with stronger input upon which to base their judgments.

Cost Saving

The present invention provides several aspects that will save time and improve the performance of highly paid experts, analysis, and attorneys. Thus, significant savings will result from use of the present invention.

Conclusion, Ramification, and Scope

Accordingly, an aspect of the present invention provides searchable annotated formatted documents that are produced by correlating documents stored as a photographic or scanned graphic representations of an actual document (evidence, report, court order, etc.) with textual version of the same documents. A produced document will provide additional details in a data structure that supports citation annotation as well as other types of analysis of a document. The data structure also supports generation of citation reports and corpus reports. A method aspect includes creating searchable annotated formatted documents including citation and corpus reports by correlating and correcting text files with photographic or scanned graphic of the original documents. Data structures for correlating and correcting text files with graphic images are valuable output by themselves. Another method aspect includes generation of citation reports, concordance reports, and corpus reports. Data structures provide for citation reports, concordance reports, and corpus reports generation.

While the above descriptions contain several specifics these should not be construed as limitations on the scope of the invention, but rather as examples of some of the preferred embodiments thereof. Many other variations are possible. For example, although U.S. patent data has been used in the examples, the document system could be applied to other categories of documents. Some embodiments would target specific types of documents. The routines could be implemented in hardware or using various software platforms. Additionally, the system could have additional features, or be used in different countries without departing from the scope and spirit of the novel features of the present invention.

Accordingly, the scope of the invention should be determined not by the illustrated embodiments, but by the appended claims and their legal equivalents. 

1. A method of generating internal citations for a formatted document, comprising the steps of: a) obtaining graphic representations of each page of the formatted document, b) optically recognizing characters on each page of the formatted document, and determining the position of the characters on each page, c) obtaining a text version of the formatted document, d) parsing the text from the text version, and e) correlating the recognized characters with the text to determine an internal citation for each sentence, wherein a data structure is created to store data determined in the correlating step.
 2. The method of claim 1 further comprising a step of: using the text to correct the recognized characters, yielding a corrected formatted document.
 3. The method of claim 1 further comprising a step of: attaching the data structure to the formatted document, wherein, when a portion of text is copied from the formatted document, a corresponding internal citation is included with the copied portion.
 4. The method of claim 3 further comprising a step of: tagging words and sentences, whereby the internal citations associated with the copied portion is directly linked to copied portion.
 5. The method of claim 3 wherein the attaching step yields a searchable annotated formatted document.
 6. The method of claim 1 wherein the data structure comprises citation start data, comprising a start column number and a start line number.
 7. The method of claim 6 wherein the data structure comprises citation end data, comprising an end column number and an end line number.
 8. The method of claim 1 wherein the data structure comprises at least one of the group of: a) citation page number, b) paragraph number, and c) citation sentence number.
 9. The method of claim 1 wherein the parsing the text step further includes at least one of the group of: a) determining document parts, b) determining document sections, c) determining new paragraphs, and d) determining paragraph types.
 10. The method of claim 1 wherein the determining the position of the characters substep further includes at least one of the group of: a) assembling lines, b) allocating lines to columns, and c) calculating line numbers.
 11. The method of claim 1 wherein the correlating step further includes at least one of the group of a) determining column numbers, b) determining line numbers, and c) determining word numbers.
 12. The method of claim 1 wherein the formatted document contains drawing figures, and wherein the correlating step further includes at least one of the group of: a) determining figure numbers in the drawing figures, and b) determining figure item numbers in drawings figures.
 13. The method of claim 1 wherein the formatted document is a patent related publication, and wherein the correlating step further includes at least one of the group of: a) determining claim numbers, and b) determining clause numbers.
 14. The method of claim 1 further comprising a step of: generating a citation document using the correlation data structure.
 15. The method of claim 1 further comprising a step of: generating a concordance using the correlation data structure.
 16. The method of claim 1 further comprising a step of: generating a corpus using the correlation data structure.
 17. The method of claim 16 wherein the corpus is based on a single word root.
 18. The method of claim 16 wherein the corpus is based on a phrase.
 19. The method of claim 16 wherein the corpus is based on a set of related words
 20. A corpus for a user specified word or phrase comprising an entry for every occurrence of the user specified word or phrase, each entry comprising: a) prior context, b) the user specified word or phrase, c) subsequent context, and d) an internal citation, wherein the internal citation identifies the document and the location inside the document where the entry is found in a formatted document. 